This statistic shows the motorway network length in the United Kingdom (UK) from 1990 to 2017, in kilometers. In the period of consideration, the length of the motorway network fluctuated. In 1990, the United Kingdom (UK) recorded a total motorway road length of *** thousand kilometers. This length rose to nearly *** thousand kilometers by 2017.

This statistic shows the breakdown of the road network in the United Kingdom (UK) from 2018 to 2020, by length of different road types. In 2020, the total road length in the United Kingdom was 247.5 thousand miles. The majority of the network is made up of minor, local roads, amounting to around 215.7 thousand miles in length in 2020.

This statistic compares the road lengths of different types of road in the United Kingdom (UK) in 2017 with the amount of road traffic they carry as percentage shares of the total values. Thus motorways, which only make up one percent of the UK's total road network, carry a fifth of the country's total traffic. On the other hand, rural minor roads make up the majority of the road network in terms of length, but only carry 14 percent of the road traffic.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 1.2963 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.3221 and 0.1183 (in million kms), corressponding to 24.8447% and 9.1242% respectively of the total road length in the dataset region. 0.856 million km or 66.031% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0104 million km of information (corressponding to 1.2101% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Public sector spending on roads in the United Kingdom was over ***** billion British pounds in 2023/24, a slight decrease when compared with the previous year. Throughout most of this time period, the amount spent on local roads is consistently higher than that spent on national roads.

The statistics refer to the volume of road traffic in Wales. Road traffic estimates for Wales are compiled by the Department for Transport on behalf of the Welsh Government. These estimates are based on annual roadside manual road traffic counts carried out across Wales during the year. These roadside counts are combined with automatic traffic count (ATC) data and road lengths to produce overall traffic estimates. Traffic estimates for major roads are based on a census of all such roads whereas traffic estimates for minor roads are estimated by calculating growth rates from a fixed sample of count points on the minor road network. Further details of the methodology are available from the DfT at the link below: https://www.gov.uk/government/publications/road-traffic-speeds-and-congestion-statistics-guidance . All surfaced roads (excluding Trunk Roads) are included in the estimates. The categories are: Major roads: Motorways. Dual carriageways designed for fast traffic with access limited to motor vehicles, and with relatively few places for joining or leaving. The only motorway in Wales is the M4. A County roads. All other A roads. Estimates for A roads are also available with sub-categories for urban and rural roads on StatsWales. Urban roads are those within the boundaries of settlements with a population of 10,000 or more, and rural roads are all other non-motorway major roads. Minor roads: B roads. Roads intended to connect different areas, and to feed traffic between A roads and smaller roads on the network. Classified unnumbered. Smaller roads intended to connect together unclassified roads with A and B roads, and often linking a housing estate or a village to the rest of the network. Similar to ‘minor roads’ on an Ordnance Survey map and sometimes known unofficially as C roads. Unclassified. Local roads intended for local traffic. The vast majority of roads fall within this category. The analysis by vehicle type is based on roadside observation where vehicles are classified according to their general appearance. The vehicle types identified are: 1) Pedal cycles: Includes all non-motorised cycles, 2) Motorcycles: Two-wheeled motor vehicles, including mopeds, motor scooters and motorcycle combinations, 3) Cars and taxis: Includes estate cars, all light vans with windows to the rear of the driver's seat, passenger vehicles with 9 seats or fewer, three-wheeled cars, motorised-invalid carriages, Land Rovers, Range Rovers and Jeeps. Cars towing caravans or trailers are counted as one vehicle, 4) Buses and coaches: Includes all public service vehicles and works buses other than vehicles with less than 10 seats, 5) Light vans: All goods vehicles up to 3,500kg gross vehicle weight. This includes all car-based vans and those of the next larger carrying-capacity, such as transit vans. Also included are ambulances, pick-ups, milk floats and pedestrian-controlled motor vehicles. Most of this group are delivery vans of one type or another, 6) Goods vehicles: All goods vehicles over 3,500kg gross vehicle weight. Includes tractors (without trailers), road-rollers, box vans and similar large vans. A two-axle motor tractor unit without trailer is also included, 7) All motor vehicles: All vehicles except pedal cycles. Traffic volume is measured using Vehicle Kilometres (VKM), which are calculated by multiplying the annual average daily flow of traffic by the corresponding length of road. For example, 1 vehicle travelling 1 kilometre a day for a year would be 365 VKM over a year. In this release estimates are presented as billion vehicle kilometres (bvk).

Compiled from Forest road network data managed by Forestry Civil Engineering. The data relates to forest road Classification.

Forest Roads are categorised on the basis of intended usage (as listed below) rather than the specification used in their construction or upgrading. This can mean that, at a particular point in time, a Class A main road or a Class B spur road may have specification features that could limit its use.

Class A - Main Roads
" Principal timber haulage route on a long-term basis. " Constructed to high specification. " Maintained to a high standard. " Limiting features shown on road map. " All year but not all weather.

Class B - Spur Roads " Used by timber haulage lorries for specific operations. " Full geometric and safety standards for stated use. " Specification tailored to suit purpose. " Possibility that surfacing not high quality or durable. " Long term maintenance minimal. " Each usage subject to individual engineering assessment. " Limiting features noted for each particular contract.

Class C - Other Roads
" Roads other than Main or Spur roads. " Maintenance dependent on usage. " Not normally used by timber haulage lorries. " Use by timber haulage lorries subject to the same individual engineering assessment as Class B roads.

Attributes;

EVT_LEN total length of classification event on road segment RD_REF number prescribed by FCE to road segment RD_CLASS current Forestry Civil Engineering classification type of road DISTRICT Forest District in which road segment occurs COUNTRY National identifier RD_NAME Local or colloquial identifier Attribution statement: Any maps produced using this data should contain the following Forestry Commission acknowledgement: "Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right [Year] Ordnance Survey [100021242]".

The statistics refer to the volume of road traffic in Wales. Road traffic estimates for Wales are compiled by the Department for Transport on behalf of the Welsh Government. These estimates are based on annual roadside manual road traffic counts carried out across Wales during the year. These roadside counts are combined with automatic traffic count (ATC) data and road lengths to produce overall traffic estimates. Traffic estimates for major roads are based on a census of all such roads whereas traffic estimates for minor roads are estimated by calculating growth rates from a fixed sample of count points on the minor road network. Further details of the methodology are available from the DfT at the link below: https://www.gov.uk/government/publications/road-traffic-speeds-and-congestion-statistics-guidance . All surfaced roads are included in the estimates. The categories are: Major roads: Motorways. Dual carriageways designed for fast traffic with access limited to motor vehicles, and with relatively few places for joining or leaving. The only motorway in Wales is the M4. A Trunk roads. Part of the strategic road network owned by and operated on behalf of Government A County roads. All other A roads. Estimates for A roads are also available with sub-categories for urban and rural roads on StatsWales. Urban roads are those within the boundaries of settlements with a population of 10,000 or more, and rural roads are all other non-motorway major roads. Minor roads: B roads. Roads intended to connect different areas, and to feed traffic between A roads and smaller roads on the network. Classified unnumbered. Smaller roads intended to connect together unclassified roads with A and B roads, and often linking a housing estate or a village to the rest of the network. Similar to ‘minor roads’ on an Ordnance Survey map and sometimes known unofficially as C roads. Unclassified. Local roads intended for local traffic. The vast majority of roads fall within this category. Traffic volume is measured using Vehicle Kilometres (VKM), which are calculated by multiplying the annual average daily flow of traffic by the corresponding length of road. For example, 1 vehicle travelling 1 kilometre a day for a year would be 365 VKM over a year. In this release estimates are presented as billion vehicle kilometres (bvk).

OpenStreetMap contains roughly 1.1 million km of roads in this region. Based on AI-mapped estimates, this is approximately 91 % of the total road length in the dataset region. The average age of data for the region is 1 year, 10 months ( Last edited 3 days ago ) and 13% of roads were added or updated in the last 6 months. Read about what this summary means : indicators , metrics

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['highway'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• highway
• surface
• smoothness
• width
• lanes
• oneway
• bridge
• layer
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

The Kinematic GPS (KGPS) data provide accurate high-resolution locational data of approximately 6400 km of roads in Great Britain using circular and/or linear transect data collected during two fieldwork campaigns (details below) carried out by the Landmap project team in order to validate the various Landmap image and elevation products. When processed, this data yields accurate 3-D coordinates that can be used for quality assessment purposes. Kinematic GPS is a technique used to enhance the precision of standard GPS, using a reference receiver of known location, such as a main road, to make corrections to the standard GPS-determined location yielding centimetre-level accuracy.

The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK, part of which was buildings data. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC.

Campaign 1

The first campaign, carried out in September 1999, required the kinematic GPS profiles for a number of pre-defined circular routes. This suited a 'Real-time Kinematic' (RTK-GPS) survey technique in which both GPS code pseudorange and carrier-phase measurements are recorded. This method is capable of yielding sub-decimetre accuracy over short baselines, generally less than 50 km.

The observing schedule was such that the reference receiver was established at a location deemed to be the centroid of the day's route so that the baseline distances from the 'local' reference receiver to mobile receiver would be kept to a minimum preventing the accumulation of distance-dependent errors. The mobile receiver would then be driven along the predefined route recording satellite observations at a rate of 5 Hz. Once the route was completed, the local reference station team was picked up and the entire team prepared to observe the next scheduled loop.

The mobile team covered almost 4,000 miles during the 14 days of the first campaign with the predefined circular routes representing some 2,800 miles (4,506km) of that total.

Campaign 2

The second campaign which took place during May and June of 2000 was geared to a different set of objectives and therefore had an observing schedule different to that of the first campaign. There was a requirement to observe some long GPS profiles that would essentially span a number of satellite-pass strips / several stereo-pair strips permitting some checking of the strip matching procedures using orthorectification techniques.

The establishment of a 'local' reference receiver station alongside each section of these proposed transects would have been too demanding in both time and logistics so an alternative processing approach was decided upon. The observing procedure was identical to that of the first campaign with the exception that the 'local' reference receiver remained in the same location for the duration of the campaign. A high-precision geodetic GPS receiver was established at a point of known co-ordinates at University College London where it collected GPS observations for the 9 days of this second campaign. The mobile receiver was driven along the required profiles recording data at a rate of 5Hz.

The routes followed for this second campaign contained a number of features as requested by the SPOT processing team that would aid them in their orthorectification tasks. One particular request was that a number of crossovers should be performed at major junctions whereby a mile or two of additional observations were taken on the feeder roads for the junction in question. Such manoeuvres provide the processing / imaging team with a greater number of features to identity and refer to as part of their orthorectification quality assessment routines. The nature of the road network in some areas meant that several long stretches of road were retraced or intersected which allowed some error checking.

Czech Directorate for Roads and Motorways (RSD) is planning to build a section of the D11 motorway in Bohemia region, Czech Republic.The project involves the construction of 15.2km four-lane highway between Hradec Králové and Smirice. The proposed highway section forms the part of D11 motorway from km90.7 to km105.9.The project is divided into two sections: first from Hradec Králové to Predmerice nad Labem with 7.61km and the second from Predmerice nad Labem to Smirice with 7.59km in length.It will includes the construction of 20 bridges with a combined length of 1.634km,two interchanges in total length of 6.431km, five noise barriers in total length of 1.475km, and the relocation and modification of 21 transport and communications units, roads and crossings of in total length of 8.678km,40 water bodies, 48 electric objects,11 pipelines and related facilities.The first section involves the construction of a long dual carriageway and one interchange in Plotiste. It will feature ordinary motorway works in open terrain with a number of crossings with the existing local transport and communications networks, requiring many small bridges, simple modifications, or relocations.The second section will also feature open terrain and will include is dual carriageway interchange (with national road III/3089 in Smirice), bridges (with two big once-over rivers: Trotin and Jordan).The project has been delayed due to lack of funding, planning and land acquisitions which resulted in the revision of investment terms from US$378.5 million to US$426 million.Environment impact assessment was secured in October 1996 and in September 2002 planning permission was granted. However, in 2013 an application was made to change the conditions of the permission for the area of Plotiste. Currently, RSD is waiting for decision with regards to this application.The project is planned to be implemented with public-private partnership (PPP).In 2011, Public consultations were held by the ministry of transport in the process of land acquisition.As of August 2014, RSD has completed the land acquisition process for about 97% of the land required for the project and is expected to complete the remaining in 2015. On August 30, 2014, RSD issued tenders for engineering services with a deadline on October 15, 2014.RSD expected to receive an approval for section two by August 2018. Read More

The Ministry of Transport (MOT) is developing the National Highways Upgrade project in Madagascar.The project involved the improvement and widening of roads. The project is being implemented on two highways RN9 and RN13.The rehabilitation of the RN9 highway comprised a total length of 107km between Toliara and Morondava.The rehabilitation of the RN13 highway comprises a total length of 492km and it is developed in two sections i) Ihosy-Ambovombé (380km) section and ii) Ambovombe-Taolagnaro (112km).It included the construction of dividers, pavements, security barriers and the installation of lighting system and safety systems.In September 2012, technical studies were finalized for the project.The project will receive funding from African Development Bank (AfDB), the European Union (EU), the Arab Bank for Economic Development in Africa (BADEA), OPEC Fund for International Development (OFID), the Export-Import Bank of Korea (KOEXIM Bank) and Japan International Cooperation Agency (JICA).In June 2014, the EPC contract for the RN9 was awarded to China railway 18th Bureau Group Co.,Ltd and int he fourth quarter of 2014, the construction activities commenced.As of September 2015, 86% of the excavation works completed on RN9.In the second quarter of 2017, the construction of RN9 was completed.In February 2016, European Union agreed to provide funding for the rehabilitation of RN13. In December 2017, the signing of a financing agreement for the rehabilitation of the RN13 took place among MOT, European Investment Bank (EIB) and European Union. EIB provided US$282 million fund for the rehabilitation of RN 6 and Ambovombe-Taolagnaro (112km) section of RN13 of which US$144 million will be in the form of loan and US$138 million as non refundable grant from European union. RN 13 Ambovombe-Taolagnaro (112km) section will get US$138 million as funds for the rehabilitation work.On December 11, 2017 a tender was announced for the extension and rehabilitation of RN13 with the submission deadline of January 9, 2018.Tender evaluation process is underway. Read More

The data published within this resource represents an ongoing dataset of automated surface condition surveys which are carried out by the City of York Council (CoYC) in order to provide an element of the information required to prioritise schemes and help to extend the life of the UK’s public infrastructure assets.

The data shown here is for road condition surveys captured within a financial year i.e. 2023/24 is for surveys between 1st April 2023 and 31st March 2024. The data published is used within wider analysis carried out by the Highways department, to help prioritise schemes to ensure more effective maintenance of highways, as well as to monitor and improve on previous maintenance techniques.

This data is provided within a spreadsheet, and ward maps within the powerpoint. Due to the nature of the underlying data, under Ordnance Survey license, we are unable to provide on Yorkview. A simple explanation of the methodology is that each section of road that has been able to be surveyed is given a score out of 100 and the mean score is for each street is calculated out of those road section scores. Roads may have been surveyed multiple times over the course of the year. The road condition scores are then split into four general categories:

• Excellent (95-100)

• Good (90-94)

• Fair (75-89)

• Poor (<75)

The data contains records which appear to be duplicated in this dataset, where a street has two road condition scores in the same ward. This is because a street can have multiple USRNs (unique street reference number) which is used to calculate the street road condition score, however City of York Council can’t publish the USRN alongside this data giving the appearance of duplicated records. Each section of road is assigned a ward based on which ward the middle of the road segment is in, so there will also be multiple scores for roads if the road crosses over a ward boundary.

It should be noted that condition data is collected covering the full CoYC road network with a condition score produced for every 10m length of carriageway surveyed. The process of averaging the data to align with the USRN does distort the output meaning that the red/poor condition band is over represented. Please note the original 10m data is used for all analysis within our Asset Management Systems.

Previous road condition data pre-March 2022 has been taken down as the supplier for the road condition data for 2022/23 and following years uses a different methodology to previous road condition data published 2016-2021.

For reporting a pothole or to look at City of York Council's responsibilities for highways please visit CYC's Roads and Pavements page.

There are also roads in the City of York Council area that are maintained by Highways England. For more information about such roads please visit the National Highways page for the North East.

Information will at a later date be held within the Council's Ward Profiles.

For further information on percentage of roads where maintenance should be considered please view the following datasets:

• % of Principal roads where maintenance should be considered (Local Recording)

• % of Non-principal classified roads where maintenance should be considered (Local Recording)

• % of Unclassified roads where maintenance should be considered (Local Recording)

OpenStreetMap contains roughly 81.1 thousand km of roads in this region. Based on AI-mapped estimates, this is approximately 84 % of the total road length in the dataset region. The average age of data for the region is 2 years ( Last edited 2 days ago ) and 7% of roads were added or updated in the last 6 months. Read about what this summary means : indicators , metrics

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['highway'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• highway
• surface
• smoothness
• width
• lanes
• oneway
• bridge
• layer
• source
• name:fr
• name:rw
• name:sw

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Snapshot img

Road Construction And Maintenance Market Size 2025-2029

The road construction and maintenance market size is forecast to increase by USD 230.4 billion, at a CAGR of 4.5% between 2024 and 2029.

The market witnesses significant growth, driven by the increasing adoption of advanced technologies and materials in infrastructure development. Innovations in asphalt paving, such as warm mix asphalt and recycled asphalt pavement, offer cost savings and environmental benefits, making them popular choices for road construction projects. Furthermore, the integration of intelligent transportation systems, including real-time traffic monitoring and predictive maintenance, enhances road safety and efficiency. Additionally, the adoption of new technologies and materials, such as asphalt mixes with recycled content and intelligent transport systems, is revolutionizing the industry, enhancing road safety and durability.
Additionally, the growing focus on public-private partnerships (PPPs) in infrastructure development offers potential collaborations for stakeholders, enabling shared risks and rewards and accelerating project execution. Sensor systems, drones, and data analytics are transforming road construction and maintenance, enabling real-time monitoring and predictive maintenance. Overall, the market continues to evolve, requiring companies to stay informed about technological advancements and market trends to effectively capitalize on opportunities and navigate challenges. The increasing demand for sustainable and cost-effective solutions, such as permeable concrete and geosynthetics, presents opportunities for companies to differentiate themselves and cater to the evolving market needs.

What will be the Size of the Road Construction And Maintenance Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market encompasses various elements, including cold mix asphalt, roadside vegetation management, risk management, drainage systems, concrete aggregates, construction technology, traffic modeling, sensor networks, and traffic simulation. Cold mix asphalt and hot mix asphalt are essential materials in road construction, with cold mix gaining popularity due to its environmental benefits and faster installation.
Drainage systems and risk management are crucial aspects of road maintenance, ensuring safety and longevity. Performance-based contracting and data acquisition enable effective tracking of project progress and performance. Corrosion protection and concrete repair are essential for maintaining the structural integrity of roads, while road safety audits ensure compliance with safety standards. Construction automation, BIM for construction, and traffic modeling streamline processes and improve efficiency. Asphalt emulsions and traffic control devices facilitate smooth traffic flow, and sensor networks provide real-time traffic data analysis. Pavement distress and traffic simulation help predict and address potential issues, reducing maintenance costs through life-cycle cost analysis.
Soil stabilization and concrete aggregates provide a stable foundation for roads, while traffic flow optimization and construction project management ensure the timely and cost-effective completion of projects. Overall, these elements shape the dynamic and evolving market.

How is this Road Construction And Maintenance Industry segmented?

The road construction and maintenance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  New construction
  Reconstruction
  Repair


Type

  Highway
  Street
  Bridge


End-user

  Government agencies
  Private contractors
  Public-private partnerships (PPP)


Geography

  North America

    US
    Canada


  Europe

    France
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The new construction segment is estimated to witness significant growth during the forecast period. The market is experiencing notable expansion in 2024, fueled by escalating infrastructure investments and the growing number of vehicles. Governments and private entities are dedicating substantial resources to road development, prioritizing sustainability and advanced technologies, including recycled asphalt mixes and intelligent transport systems, to improve road safety and longevity. However, escalating construction costs, encompassing raw materials and labor, pose challenges to market growth. Prominent projects are shaping the road infrastructure landscape in 2024. The I-70 Widening: Wentzville to Independence pr

The Ministry of Infrastructure and Transport of Italy (MIT) is undertaking the Turin to Milan motorway upgrade project in Italy.The project involves the upgrade of a 57km long section of the A4 toll motorway between Turin, starting at Greggio and Milan. This project includes the widening and upgrading safety standards over the whole length of the highway, and the construction of a new 5km alignment at Bernate and 15km safety lane between the Malpensa Airport link and the Milano ring road.The project will additionally include the construction of four lane roads, footpaths, renovation of a bridge and installation of towers and traffic monitoring systems.Autostrada Torino Milano SpA S.p.A through the subsidiary Satap SpA has been appointed as EPC contractor for the project.In the fourth quarter of 2009, construction works were commenced. In January 2010, The European Investment Bank (EIB) funded US$350 million for the project. Read More